Developed by Robert Smith - download

The ideal observer is
a type of analysis that measures the

precision of a signal. To measure the precision of a neural

signal, a recording is made of the neuron's response to a pair

of repeating stimuli. Half of the responses are used to construct a probability
histogram of the response amplitude, which defines the variability of the
neural response to the stimuli. The other half of the responses are used to
probe the histogram. The probability that a response was generated by one
stimulus is compared with the probability that it was generated by the other
stimulus. The one with the highest probability is chosen as the most likely
stimulus, and the fraction of correct responses is calculated. When performed

for several pairs of stimuli varying e.g. in their contrast or

another parameter, this process generates a "neurometric function"
that defines the neuron's ability to transmit information.

The "ioprogs"
distribution contains several programs useful in

ideal observer analysis.